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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Health Sciences / Faculty of Health Sciences >
Peer-reviewed Journal Articles, etc >
Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model
| Title: | Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model |
| Authors: | Yoshimura, Takaaki Browse this author →KAKEN DB | | Hasegawa, Atsushi Browse this author | | Kogame, Shoki Browse this author | | Magota, Keiichi Browse this author →KAKEN DB | | Kimura, Rina Browse this author | | Watanabe, Shiro Browse this author →KAKEN DB | | Hirata, Kenji Browse this author →KAKEN DB | | Sugimori, Hiroyuki Browse this author →KAKEN DB |
| Keywords: | deep learning | | PET | | radiation exposure | | super-resolution |
| Issue Date: | 31-Mar-2022 |
| Publisher: | MDPI |
| Journal Title: | Diagnostics |
| Volume: | 12 |
| Issue: | 4 |
| Start Page: | 872 |
| Publisher DOI: | 10.3390/diagnostics12040872 |
| PMID: | 35453920 |
| Abstract: | In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, the aim of this study was to clarify whether the SR deep learning technique could improve the image quality of the 50%-acquisition-time image to the level of that of the 100%-acquisition-time image. One-hundred-and-eight adult patients were enrolled in this retrospective observational study. The supervised data were divided into nine subsets for nested cross-validation. The mean peak signal-to-noise ratio and structural similarity in the SR-PET image were 31.3 dB and 0.931, respectively. The mean opinion scores of the 50% PET image, SR-PET image, and 100% PET image were 3.41, 3.96, and 4.23 for the lung level, 3.31, 3.80, and 4.27 for the liver level, and 3.08, 3.67, and 3.94 for the bowel level, respectively. Thus, the SR-PET image was more similar to the 100% PET image and subjectively improved the image quality, as compared to the 50% PET image. The use of the SR deep-learning technique can reduce the injected FDG dose and thus lower radiation exposure. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/85539 |
| Appears in Collections: | 保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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